def SIFTgrain(self, octave):
path_analyse = '/CT_analysesClassification/' + octave
octaves = 6
initial_sigma = 1.1
gauss = GaussianSmoothing3D(self.path + path_analyse, octaves).smoothing(initial_sigma)
dimension = 3
dog = DoG(self.path + path_analyse, dimension)
dog.apply()
pathDoG = '/3DDoG/'
local_extrema = LocalExterma3D(self.path + path_analyse + pathDoG, self.path + path_analyse, True)
local_extrema.find()
path_local_extrema = '/3DLocalExtremum/'
extrema = ExtremaSpace3D(self.path + path_analyse + pathDoG + path_local_extrema)
extrema.find()
Hessian = HessianMatrix(5.0)
Hessian.HessianElimination(self.path + path_analyse)
keypointorientation = KeyPointOrientation(self.path + path_analyse)
keypointorientation.apply()
path_keypoint_orientation = '/KeyPointsOrientation/'
images = ReadImage(self.path + path_analyse + path_keypoint_orientation).openImage()
for im in images:
rotateImage(im, 10, self.path + path_analyse).apply()
path_discriptor = '/Descriptor3D/'
keydis = KeypointsFeatures(self.path + path_analyse + path_discriptor, self.path + path_analyse).apply()
def SIFTgrain(self, octave):
path_analyse = '/CT_analysesClassification/' + octave
octaves = 6
initial_sigma = 1.1
gauss = GaussianSmoothing3D(self.path + path_analyse,
octaves).smoothing(initial_sigma)
dimension = 3
dog = DoG(self.path + path_analyse, dimension)
dog.apply()
pathDoG = '/3DDoG/'
local_extrema = LocalExterma3D(self.path + path_analyse + pathDoG,
self.path + path_analyse, True)
local_extrema.find()
path_local_extrema = '/3DLocalExtremum/'
extrema = ExtremaSpace3D(self.path + path_analyse + pathDoG +
path_local_extrema)
extrema.find()
Hessian = HessianMatrix(5.0)
Hessian.HessianElimination(self.path + path_analyse)
keypointorientation = KeyPointOrientation(self.path + path_analyse)
keypointorientation.apply()
path_keypoint_orientation = '/KeyPointsOrientation/'
images = ReadImage(self.path + path_analyse +
path_keypoint_orientation).openImage()
for im in images:
rotateImage(im, 10, self.path + path_analyse).apply()
path_discriptor = '/Descriptor3D/'
keydis = KeypointsFeatures(self.path + path_analyse + path_discriptor,
self.path + path_analyse).apply()
def test_case3():
assert rotateImage.rotateImage([[10, 9, 6, 3, 7], [6, 10, 2, 9, 7],
[7, 6, 3, 8, 2], [8, 9, 7, 9, 9],
[6, 8, 6, 8, 2]]) == [[6, 8, 7, 6, 10],
[8, 9, 6, 10, 9],
[6, 7, 3, 2, 6],
[8, 9, 8, 9, 3],
[2, 9, 2, 7, 7]]
def findTestType2(imageName, imageInitialName, dimX, dimY):
# Se citeste imaginea
image = cv2.imread(imageInitialName)
# Se determina cooronatele celor 8 colturi ale grilelor existente in imagine
(x1, y1, x2, y2, x3, y3, x4, y4, x5, y5, x6, y6, x7, y7, x8, y8,
_) = rotateImage(imageName)
valueX = int((x8 * dimX) / 620)
valueY = int((y8 * dimY) / 870)
imageTestType = image[valueY - 280:valueY - 3, valueX - 120:valueX + 10]
# Se decupeaza locul in care se gasesc cele doua casute pentru bifa de Fizica / Informatica, din imaginea initiala
# care a fost citita in variabila image
cv2.imwrite('images/imgType.jpg', imageTestType)
def findTestType(imgName):
# Se defineste un filtu de sharpening al imaginii
kernelSharpening = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
# Se citeste imaginea
image = cv2.imread(imgName)
# Se redimensioneaza imaginea
image = cv2.resize(image, (620, 870), interpolation=cv2.INTER_AREA)
# Se determina cooronatele celor 8 colturi ale grilelor existente in imagine
(x1, y1, x2, y2, x3, y3, x4, y4, x5, y5, x6, y6, x7, y7, x8, y8,
rotateNumber) = rotateImage(imgName)
# Se decupeaza locul in care se gasesc cele doua casute pentru bifa de Fizica / Informatica
imageTestType = image[y8 - 65:y8 - 3, x8 - 35:x8 + 10]
# Se aplica imaginii filtrul de accentuare a detaliilor, kernelSharpening, definit anterior
imageTestType = cv2.filter2D(imageTestType, -1, kernelSharpening)
# Se prelucreaza acesta imagine decupata pentru calcularea maximului intensitatilor pixelilor
# Se transforma imaginea din RGB in imagine in tonuri de gri
grayImageTestType = cv2.cvtColor(imageTestType, cv2.COLOR_BGR2GRAY)
# Se aplica un filtru de blurare a imaginii
grayImageTestType = cv2.GaussianBlur(grayImageTestType, (3, 3), 0)
# Dorim acum detectarea marginilor din imagine, folosind functia Canny, asupra imaginii in tonuri de gri
edgedImageTestType = cv2.Canny(grayImageTestType, 10, 250)
# Vom construi un nucleu 'kernel' folosind functia getStructuringElement care va returna un element de o
# anumita dimensiune si forma pentru anumite operatii morfologice
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
# In continuare vom aplica nucleul creat la pasul anterior intr-o transformare morfologica de tipul specificat
# prin parametrul MORPH_CLOSE (operatie de inchidere), asupra imaginii produse de aplicarea edge detectorului
# Canny. Functia folosita pentru aceasta transformare este morphologyEx
closedImageTestType = cv2.morphologyEx(edgedImageTestType, cv2.MORPH_CLOSE,
kernel)
lenY = closedImageTestType.shape[0]
# Se calculeaza suma pixelilor din jumatatea de sus, respectiv jumatatea de jos in variabilele sumPixels1,
# sumPixels2
sumPixels1 = 0
for i in range(0, int(lenY / 2)):
sumPixels1 = sumPixels1 + np.sum(np.sum(closedImageTestType[i]))
sumPixels2 = 0
for i in range(int(lenY / 2), lenY):
sumPixels2 = sumPixels2 + np.sum(np.sum(closedImageTestType[i]))
# Daca maximul este sumPixels1, adica in jumtatatea de sus => raspunsul este Informatica;
# altfel => raspunsul este Fizica
if sumPixels1 > sumPixels2:
return "Informatica"
else:
return "Fizica"
rotationDuration = 5
'''
rotationDuration is the length of time the rotation occurs between certain key frames
example
image starts at 0 secs and goes until 1 sec and flips one time.
rotationDuration would be 1 (for 1 sec)
image starts at 0 secs and goes until 2 secs BUT there's a change in the roation at the 1 sec mark
roationDuration would need to be set twice and would be 1 sec both times.
'''
rotationDurationPerFrame = round(float(rotationDuration)/frameRate,6)
amountToRatePerFrame = float(rotation)/frameRate
startingRotation = 0
while (startTime < rotationDuration):
rotatedImage = rotateImage.rotateImage(rotatingImage,startTime,rotationDurationPerFrame,startingRotation)
countingRotation = countingRotation + 1
startTime = startTime + rotationDurationPerFrame
startingRotation = startingRotation + amountToRatePerFrame
imageArray.append(rotatedImage)
final = CompositeVideoClip(imageArray, size = moviesize)
final.set_duration(5).write_videofile("exports/rotationTest_5secs.mp4", fps=frameRate,codec='mpeg4',bitrate="4000k",audio_codec="mp3")
def test_case1():
assert rotateImage.rotateImage([[1, 2, 3], [4, 5, 6], [7, 8,
9]]) == [[7, 4, 1],
[8, 5, 2],
[9, 6, 3]]
def test_case2():
assert rotateImage.rotateImage([[1]]) == [[1]]
def setUp(self):
self.open = ReadImage(
'./test_data/1_nd/CT_analyses/KeyPointsOrientation/').openImage()
self.rotate = rotateImage(self.open[0], 10,
'./test_data/1_nd/CT_analyses/')
def setUp(self):
self.open = ReadImage('./test_data/1_nd/CT_analyses/KeyPointsOrientation/').openImage()
self.rotate = rotateImage(self.open[0],10,'./test_data/1_nd/CT_analyses/')